def Train(self, inputs, targets):
# Calc errors
self.FeedForward(inputs)
targets = Matrix.FromArray(targets)
output_errors = Matrix.Subtract(targets, self.outputs)
weights_ho_T = Matrix.Transpose(self.weights_ho)
hidden_errors = Matrix.Multiply(weights_ho_T, output_errors)
# Calc gradients
gradient_o = Matrix.StaticMap(self.outputs, dsigmoid)
gradient_o.ElementMultiply(output_errors)
gradient_o.ElementMultiply(self.lr)
hidden_T = Matrix.Transpose(self.hidden)
delta_weights_ho = Matrix.Multiply(gradient_o, hidden_T)
gradient_h = Matrix.StaticMap(self.hidden, dsigmoid)
gradient_h.ElementMultiply(hidden_errors)
gradient_h.ElementMultiply(self.lr)
input_T = Matrix.Transpose(self.inputs)
delta_weights_ih = Matrix.Multiply(gradient_h, input_T)
# Adjusting weights and biases
# self.weights_ih.Display()
self.weights_ih.Add(delta_weights_ih)
self.bias_h.Add(gradient_h)
# delta_weights_ho.Display()
self.weights_ho.Add(delta_weights_ho)
self.bias_o.Add(gradient_o)
def FeedForward(self, arr):
# Hidden nodes
self.inputs = Matrix.FromArray(arr)
self.hidden = Matrix.Multiply(self.weights_ih, self.inputs)
self.hidden.Add(self.bias_h)
self.hidden.Map(sigmoid)
# Output nodes
self.outputs = Matrix.Multiply(self.weights_ho, self.hidden)
self.outputs.Add(self.bias_o)
self.outputs.Map(sigmoid)
return self.outputs
